Abstract
In this paper we present a statistical approach for localization and classification of 3-D objects in 2-D images with real heterogeneous background. Two-dimensional local feature vectors are computed directly from pixel intensities in square gray level images with the wavelet multiresolution analysis. We use three different resolution levels for the feature computation. For the first one local neighborhoods of size 8 × 8 pixels, for the second one 4 × 4 pixels, and for the third one 2 × 2 pixels are taken into account. Then we define an object area as a function of 3-D transformations and represent the feature vectors as density functions. Our localization and classification algorithm uses a combination of object models created for the three different resolutions in the training phase. Experiments made on a real data set with 42240 images show that the recognition rates are much better using the resolution combination of the wavelet transformation.
This work was partly funded by the German Research Foundation (DFG) Graduate Research Center 3D Image Analysis and Synthesis
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Grzegorzek, M., Reinhold, M., Niemann, H. (2005). Feature Extraction with Wavelet Transformation for Statistical Object Recognition. In: Kurzyński, M., Puchała, E., Woźniak, M., żołnierek, A. (eds) Computer Recognition Systems. Advances in Soft Computing, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32390-2_17
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DOI: https://doi.org/10.1007/3-540-32390-2_17
Publisher Name: Springer, Berlin, Heidelberg
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